Using Big Data to Understand the Human Condition: The Kavli Human Project

David Steinberg. Azmak et al. introduce the Kavli HUMAN Project (KHP), a unique and ambitious attempt to exploit big-data health analytics to study factors that contribute to good health. The KHP differs dramatically from typical large scale health studies in the depth of data that will be collected. Most such programs focus on very specific … Continue reading Using Big Data to Understand the Human Condition: The Kavli Human Project

Advertisements

A Bayesian Semiparametric Framework for Understanding and Predicting Customer Base Dynamics

David Steinberg.  Dew and Ansari look at how to automate customer analytics. This can be a crucial activity for companies that manage distinct customer bases. In these data-rich and dynamic settings, visualization is essential for understanding events of interest. The value of visualization has led to the popularity of analytics dashboards. Although popular in practice, … Continue reading A Bayesian Semiparametric Framework for Understanding and Predicting Customer Base Dynamics

Probabilistic Segmentation via Total Variation Regularization

David Steinberg.  Wytock and Kolter take on a challenging problem in time series analysis – segmentation. The goal is to partition the time series into subsets of observations which can be regarded as generated by the same distribution. A particular example is the popular hidden Markov model, in which the partition is given by the … Continue reading Probabilistic Segmentation via Total Variation Regularization

Deep Kalman Filters

David Steinberg.  Kalman Filters are a popular and influential approach for modeling time-varying phenomena. They admit an intuitive probabilistic interpretation, have a simple functional form, and have been successfully applied in a wide variety of disciplines. The classic Kalman filter is a generative dynamic model in which the state of the system evolves over time … Continue reading Deep Kalman Filters